Using machine learning to predict patients with polycystic ovary disease in Chinese women.

Journal: Taiwanese journal of obstetrics & gynecology
PMID: 39794054

Abstract

OBJECTIVE: With an estimated global frequency ranging from5 % to 21 %, polycystic ovary syndrome (PCOS) is one of the most prevalent hormonal disorders. There are many factors found to be related to PCOS. However, most of these researches used traditional methods such as multiple logistic regression (LR). Nowadays, machine learning (Mach-L) emerges as a new method and can be used in medical researches. In the present study, there were two goals: 1. Compare the accuracy of five alternative Mach-L techniques with that of conventional LR. 2. Use Mach-L to forecast PCOS and prioritize the risk factors.

Authors

  • Chen-Yu Wang
    Department of Digestive Tumor, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.
  • Dee Pei
    Division of Endocrinology and Metabolism, Department of Internal Medicine, Fu Jen Catholic University Hospital, School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei City 242, Taiwan, R.O.C.
  • Chun-Kai Wang
    Department of Obstetrics and Gynecology, Zuoying Branch of Kaohsiung Armed Forces General Hospital, Kaohsiung, Taiwan.
  • Jyun-Cheng Ke
    Department of Obstetrics and Gynecology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.
  • Siou-Ting Lee
    Department of Obstetrics and Gynecology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan; Department of Obstetrics and Gynecology, Taoyuan Armed Forces General Hospital, Taoyuan, Taiwan.
  • Ta-Wei Chu
    Department of Obstetrics and Gynecology, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan, R.O.C.
  • Yao-Jen Liang
    Graduate Institute of Applied Science and Engineering, Fu Jen Catholic University, New Taipei City 242, Taiwan, R.O.C.

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